We consider joint subchannel and power allocation for an orthogonal frequency division multiple access (OFDMA)-based cognitive radio network. We formulate the downlink resource allocation problem as a spectral-footprint (bandwidth-power product) minimization problem under interference threshold at primary users, total power and quality of service constraints. The cognitive base station solves this non-convex mixed-integer programming problem iteratively by dividing it into a subchannel allocation master problem and power allocation subproblems. The subchannel assignment problem is solved by applying a modified Hungarian algorithm while the power allocation subproblems are solved by using Lagrangian techniques. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. The performance of our spectral-footprint minimization technique is compared with the waterfilling power allocation.

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.

The performance of optimal switching adaptive modulation for multiuser amplify-and-forward (AF) relay networks over Nakagami-m fading channel is studied. Moreover, we consider two types of communication: i) relay-assisted communication (RAC): the source transmits its message to best user via relay path, ii) selective communication (SC): the source decides whether to forward the source message to the best user via relay path or direct path by comparing the end-to-end instantaneous signal-to-noise ratio (SNR) at the best user, which is independent of modulation scheme. Specifically, multiuser diversity and adaptive discrete-rate five-mode M-ary quadrature amplitude modulation (M-QAM) with constant transmit power is employed with fixed and optimal switching thresholds. The optimization criterion for switching thresholds is the maximization of spectral efficiency subject to target bit-error-rate. In particular, the detrimental effect of feedback delays in multiuser opportunistic scheduling (which exploits multiuser diversity) and adaptive modulation is quantified. To this end, an exact and an upper bound of the end-to-end SNRs are obtained and used to derive the cumulative distribution function and probability density function for each case. Moreover, exact outage probabilities, and lower bounds for the outage probabilities and average bit error rates, and the average spectral efficiencies are derived in closed-form. Further, we also present the high SNR approximations for outage probabilities and BERs. We observe that, for a particular spectral efficiency, adaptive five-mode M-QAM with optimal switching outperforms fixed switching and provides approximately 2 dB and 2.5 dB gain in average SNR for RAC and SC, respectively. Monte-Carlo simulations are performed to validate the analytical results.

We consider novel cooperative spectrum sensing (CSS) algorithms based on the pattern classification techniques for cognitive radio (CR) networks. In this regard, support vector machine (SVM) and weighted K-nearest-neighbor (KNN) classification techniques are implemented for CSS. The received signal strength at the CR users are treated as features and fed into the classifier to detect the availability of the primary user (PU). Each instance of PU activity (i.e., availability and unavailability) is categorized into positive and negative classes (respectively). In the case of SVM, for minimization of classification errors the support vectors are obtained by maximizing the margin between the separating hyperplane and data. Towards this end, we investigate the effect of different kernels through quantifying in terms of detection probability by representing the receiver operating characteristic (ROC) curves. Furthermore, weighted KNN classification technique is proposed for CSS and the corresponding weights are calculated by evaluating the area under ROC curve of each feature. Our comparative results clearly reveal that the proposed SVM and weighted KNN algorithms outperform the existing state-of-the-art pattern classification-based CSS techniques.

This paper evaluates the performance of adaptive modulation in multi-relay networks with selective relaying, under Nakagami-m fading. In the system model, the source decides independently whether to forward the source message to the destination via the best (partial relay selection) relay path or direct path by comparing the end-to-end instantaneous signal-to-noise ratio (SNR) at the destination, which is independent of the modulation scheme. Adaptive discrete-rate M-ary quadrature amplitude modulation with fixed switching thresholds is implemented by dividing the SNR region into five modes. In particular, impact of imperfect (outdated) channel estimation due to feedback delay is quantified for relay selection. We derive the cumulative distribution function for the upper-bound of end-to-end SNR in closed-form. Further, lower-bounds of outage probability and average bit error rate, and upper-bound of spectral efficiency are derived in closed-forms. Monte Carlo simulation results validate our numerical analysis.

In this paper, the performance of adaptive modulation for multiuser amplify-and-forward relay networks over Nakagami-m fading is studied. Specifically, adaptive discrete rate five-mode M-ary quadrature amplitude modulation is employed with fixed switching thresholds. In particular, the detrimental effect of feedback delays in multiuser scheduling is quantified. To this end, an upper bound of the end-to-end signal-to-noise ratio (SNR) is obtained and used to derive the cumulative distribution function, probability density function and moment generating function. Moreover, lower bounds for the outage probability and average bit error rate, and upper bounds for the spectral efficiency and amount of fading are derived in closed-form. Our lower and upper bounds are asymptotically exact at high SNRs, and hence, provide accurate assessment of the system performance.